{"title":"用翻译模型改进英越词对齐","authors":"Giang Nguyen, Dinh Dien","doi":"10.1109/rivf.2012.6169841","DOIUrl":null,"url":null,"abstract":"Word alignment for a parallel corpus is the connection between the words/phrases in source language and the words/phrases in target language. The alignment result is an important input for many natural language processing applications. In this paper, we propose an approach to improve the English-Vietnamese word alignment result by using the alignment frequency that is presented in the translation model of SMT (Statistical Machine Translation). We also indicate 5 common error types of English-Vietnamese word alignment and propose the heuristic patterns to discover the alignment errors. The experimental results show the improvement compared to the result of GIZA++.","PeriodicalId":115212,"journal":{"name":"2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Improving English-Vietnamese Word Alignment Using Translation Model\",\"authors\":\"Giang Nguyen, Dinh Dien\",\"doi\":\"10.1109/rivf.2012.6169841\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Word alignment for a parallel corpus is the connection between the words/phrases in source language and the words/phrases in target language. The alignment result is an important input for many natural language processing applications. In this paper, we propose an approach to improve the English-Vietnamese word alignment result by using the alignment frequency that is presented in the translation model of SMT (Statistical Machine Translation). We also indicate 5 common error types of English-Vietnamese word alignment and propose the heuristic patterns to discover the alignment errors. The experimental results show the improvement compared to the result of GIZA++.\",\"PeriodicalId\":115212,\"journal\":{\"name\":\"2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/rivf.2012.6169841\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/rivf.2012.6169841","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving English-Vietnamese Word Alignment Using Translation Model
Word alignment for a parallel corpus is the connection between the words/phrases in source language and the words/phrases in target language. The alignment result is an important input for many natural language processing applications. In this paper, we propose an approach to improve the English-Vietnamese word alignment result by using the alignment frequency that is presented in the translation model of SMT (Statistical Machine Translation). We also indicate 5 common error types of English-Vietnamese word alignment and propose the heuristic patterns to discover the alignment errors. The experimental results show the improvement compared to the result of GIZA++.